Efficient capacitance extraction computations in wavelet domain
Why this work is in the frame
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Bibliographic record
Abstract
A new approach is presented for efficient capacitance extraction. This technique utilizes wavelet bases and is kernel independent. The main benefits of the proposed technique are as follows: (1) it takes a full advantage of the multiresolution analysis and gives accurate total charge on a conductor without obtaining an accurate solution for the charge density per se; (2) the method employs an extremely aggressive thresholding algorithm and compresses the stiffness matrix to an almost diagonal sparse matrix; and (3) construction of the stiffness matrix is performed iteratively, which facilitates easy and simple control of convergence and provides means of trading accuracy for speed. The proposed method has computational cost of O(N), versus O(N/sup 3/) for conventional methods. The proposed algorithm has a major impact on the speed and accuracy of physical interconnect parameter extraction with speedup reaching 10/sup 3/ for even moderately sized problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it